Name
Abstract | ||
|---|---|---|
Song-selection and mood are interdependent. If we capture a song's sentiment, we can determine the mood of the listener, which can serve as a basis for recommendation systems. Songs are generally classified according to genres, which don't entirely reflect sentiments. Thus, we require an unsupervised scheme to mine them. Sentiments are classified into either two (positive/negative) or multiple (happy/angry/sad/...) classes, depending on the application. We are interested in analyzing the feelings invoked by a song, involving multi-class sentiments. To mine the hidden sentimental structure behind a song, in terms of "topics", we consider its lyrics and use Latent Dirichlet Allocation (LDA). Each song is a mixture of moods. Topics mined by LDA can represent moods. Thus we get a scheme of collecting similar-mood songs. For validation, we use a dataset of songs containing 6 moods annotated by users of a particular website. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24800-9_31 | IDA |
Keywords | Field | DocType |
hidden sentimental structure,latent dirichlet allocation,recommendation system,similar-mood song,mining sentiment,particular website,multi-class sentiment,unsupervised scheme | Recommender system,Mood,Latent Dirichlet allocation,Music theory,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Lyrics,Machine learning,Feeling | Conference |
Volume | ISSN | Citations |
7014 | 0302-9743 | 3 |
PageRank | References | Authors |
0.44 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Govind Sharma | 1 | 5 | 1.95 |
| M. Narasimha Murty | 2 | 824 | 86.07 |